Project Summary: Up to 30% of all cancer deaths in the United Slates may be attributable to obesity. Environmental and policy supports for physical activity and healthy eating represent promising strategies to curb the rise in obesity rates and related cancers. Mounting research suggests that the built environment can facilitate or constrain physical activity and healthy eating, particulariy for low-income and minority populations. However, virtually all of this research has examined environrhental infiuences among residential settings, disregarding the work environment where employed adults spend much of their time, The overall goal of this project is to understand how environments and policies where employed adults live and work are associated with obesity. This goal will be accomplished by addressing the following aims: (1) Develop and test the reliability and validity of selfreported instruments for assessing worksite environments and policies relevant for physical activity and diet behaviors; (2) Examine whether specific types and number of worksite supports for physical activity and healthy eating are predictive of obesity; (3) Examine whether perceived and objectively measured characteristics of the built environment in the residential and worksite neighborhood are independently and jointly associated with body mass index (BMI); and (4) Disseminate findings to local worksites, governments, and practitioners. Employed adults (n=2000) will be sampled among randomly selected census tracts in four metropolitan areas in Missouri, with oversampling of high racial/ethnic minority and high population density census tracts. Data on self-reported behaviors, BMI, and perceived environmental and organizational supports for physical activity and healthy eating will be derived from a targeted random-digit-dial telephone survey. Accelerometry will be collected on a sub-sample of the study population. Participants' data will be linked with contextual poverty, environmental and policy data derived using Geographic Information Systems, and multilevel modeling will be used to analyze the nested data. Findings will be disseminated among urban planners, employers, and policymakers to encourage the translation of evidence into practice and policies.